Combining sampling and autoregression for motion synthesis

We present a novel approach to motion synthesis. It is shown that by splitting sequences into segments new sequences can be created with a similar look and feel to the original. Copying segments of the original data generates a sequence which maintains detailed characteristics. By modelling each segment using an autoregressive process we can introduce new segments and therefore unseen motions. These statistical models allow a potentially infinite number of new segments to be generated. We show that this system can model complicated nonstationary sequences which a single ARP is unable to do